Role-Conditioned Value Attribution in LLMs

• Role-Conditioned Value Attribution is a framework that quantifies how LLMs adjust their value judgments based on explicit role or persona inputs using role embeddings.
• The methodology combines formal prompt design, embedding-based role representation, and empirical metrics to assess model performance in moral reasoning, information retrieval, and legal summarization.
• Empirical findings indicate strong value inertia in LLMs, highlighting the need for role-aware fine-tuning and strategic prompt engineering for robust, context-sensitive applications.

Role-Conditioned Value Attribution is the systematic study and quantification of how LLMs modulate their value-laden outputs—especially judgments, moral stances, and relevance assessments—when conditioned on explicit role or persona information. This area interrogates both the fidelity of model adaptation to assigned roles and the persistence of model-internal value inertia under such conditioning. Analyzing role-conditioned value attribution combines formal prompt design, embedding-based role representation, output aggregation, and empirical evaluation across multiple domains such as moral psychology, information retrieval, conversational role-play, and legal summarization.

1. Formal Definitions and Theoretical Basis

Role-conditioned value attribution measures how a model’s output—typically in the form of a value or moral judgment—varies when the same input is presented under different “roles” or “personas”. In this paradigm, roles are structured as explicit variable vectors, $r \in \mathbb{R}^k$, capturing demographic or characterological attributes (e.g., age, gender, occupation, culture, religion), which are injected at the prompt level, such that the LLM becomes a conditional generator, $f_\theta(\cdot| r)$ (Lee et al., 2024).

The key constructs are:

• Role embedding $r$: One-hot or learned encoding of $k$ demographic or character traits.
• Value-orientation vector $v_r$: For each role $r$, a vector in $\mathbb{R}^d$ where $d$ indexes value or moral foundations (e.g., Harm, Fairness, Authority).
• Baseline orientation $v_\mathrm{baseline}$: Model outputs under a neutral role $r_0$, often an "empty" or default prompt.
• Distance metrics: $\Delta v(r) = v_r - v_\mathrm{baseline}$, with $\|\Delta v(r)\|_2$ and $1 - \cos(v_r, v_\mathrm{baseline})$ quantifying role-driven deviation.
• Variance across roles: $$\mathrm{Var}(v) = \frac{1}{N} \sum_{j=1}^N \|v_{r_j} - \overline{v}\|_2^2$$, capturing the stability or inertia of the model’s value orientation.

The approach distinguishes itself from generic prompting by explicitly sampling and injecting diverse $r$ from pre-defined demographic or structured character spaces, aggregating and comparing outputs across many such conditions (Lee et al., 2024, Jun et al., 8 Jan 2026).

2. Role Encoding and Attribute Schema

Advanced frameworks formalize role as multidimensional “character identity,” divided into:

• Parametric Identity: Pre-trained knowledge about widely-known or famous personas, directly encoded in model parameters.
• Attributive Identity: Fine-grained, scenario-specific behavioral, moral, and interpersonal attributes, constructed as structured JSON schemas with tens of fields spanning personality, motivations, abilities, and relationships (Jun et al., 8 Jan 2026).

Roles are sampled from comprehensive attribute sets—demographic for persona-based experiments, or composite profiles (character "schemas") for role-playing agents. Each schema operationalizes persona as a vector or structured object, supporting both single-turn and multi-turn interrogation (Jun et al., 8 Jan 2026).

3. Methodologies and Experimental Protocols

A. Moral and Value Orientation

• Questionnaires: Items from MFQ-30 (moral foundations; $d=5$) and PVQ-RR (personal values; $d=10$).
• Mean Rating Correction (MRAT): For response $x_i(r)\in\{0,1,2,3,4,5\}$ under role $r$, scores are mean-centered across all items and then aggregated within value dimensions:

$$\mu_r = \frac{1}{|D|} \sum_{i\in \text{all items}} x_i(r)$$

$$s_d(r) = \frac{1}{|D_d|} \sum_{i\in D_d} [x_i(r) - \mu_r]$$

• Aggregate metrics: Across $N$ role draws, compute mean and variance of $v_r$ to diagnose inertia (Lee et al., 2024).

B. Information Retrieval and Relevance Attribution

• Prompt manipulation: Roles constructed as adjectives, adverbs, modals inserted into ranking prompts.
• Causal patching: Mechanistic interpretability combines activation patching and attention-head ablation to trace where in the transformer stack role information is injected and impacts downstream relevance judgments (Wang et al., 20 Oct 2025).
• Metric deltas: Role-play-driven performance differences (e.g., $\Delta M_\mathrm{role}$ for nDCG@10) quantify the functional impact.

C. Role-Playing Agents

• Unified character profiles: Structured schemas for both famous and synthetic personas.
• Benchmarks: PersonaGym (single-turn, action/consistency/toxicity), CoSER (multi-turn, fidelity/anthropomorphism).
• Attention-lift metrics: Quantify which context segments (profile/history/generated) dominate the model’s internal computation over interaction turns (Jun et al., 8 Jan 2026).

D. Reinforcement Learning for Role-Conditioned Attribution

• RLVR framework: Multi-dimensional, verifiable rewards (focus selection, reference overlap, character-conditioned normalization) drive policy optimization for character-consistent output (Tang et al., 8 Jan 2026).
• Group Relative Policy Optimization (GRPO): Normalizes rewards by role, improving gradient stability and attribution fidelity.

4. Empirical Findings Across Domains

A. Moral/Value Inertia

Extensive role-play-at-scale experiments show that LLMs retain strong, stable value orientations under diverse persona prompts. For all major models, ≥70–90% of responses in key dimensions (Harm, Fairness) concentrate on a single bucket, revealing pronounced value inertia. Aggregate statistics across persona samples converge with increasing $N$, and inter-set comparisons yield near-perfect overlap (Pearson $r > 0.98$), indicating weak sensitivity to sampled roles (Lee et al., 2024).

B. Structural Role Effects in Relevance Judgment

Role-play signals in IR prompts are encoded in early transformer layers (layers 1–5) and strongly influence downstream relevance scoring. Adverbs in the prompt (e.g., "accurately") have the largest effect, followed by adjectives, while modals contribute little. Activation patching pinpoints specific attention heads as carriers of the role signal, and ablating these leads to significant drops ($\Delta$LD, up to –0.22 nDCG) in model performance for role-consistent versus role-inconsistent prompts (Wang et al., 20 Oct 2025).

C. Parametric vs. Attributive Identity in Role-Playing Agents

• Fame Fades: Famous characters initially benefit from stronger parametric priors, but this advantage decays within 24 turns of dialogue as conversational context is accumulated and model attention shifts to generated history (Jun et al., 8 Jan 2026).
• Nature Remains: General personality traits are robustly portrayed across positive and negative valence. However, motivations linked to morality and negative interpersonal relationships are sensitive bottlenecks; models underperform on negative-polarity fields, correlated with low attribute attention.

D. Role-Conditioned Motivated Reasoning in Legal Summarization

Legal model outputs conditioned on stakeholder roles (judge, prosecutor, attorney) show systematic motivated reasoning. Adversarial roles produce higher fact/argument bias and reduced inclusion (e.g., $\overline{\text{FactIncl}}_{judge}=0.92$ vs. $\overline{\text{FactIncl}}_{defense}=0.80$; bias scores 0.5 higher for adversarial attorneys). Prompts instructing balance are insufficient to eliminate role-consistent strategic framing, raising concerns for objectivity in high-stakes deployments (Cho et al., 30 Aug 2025).

5. Theoretical and Mechanistic Insights

Role-Conditioned Value Attribution reveals that LLMs’ core moral and value orientations are anchored in pre-training distributions and RLHF objectives, which favor certain individualizing principles (e.g., harm avoidance, fairness). Role information, injected at the prompt, is strong enough to drive stylistic and surface-level adaptation but insufficient to alter deeper statistical regularities. Mechanistically, role conditioning is absorbed and aggregated early in model computation, with critical prompt components (notably, adverbs and adjectives) channeling most of the causal influence (Lee et al., 2024, Wang et al., 20 Oct 2025).

In interactive, turn-based scenarios, initial role-congruent behavior decays unless grounded by dynamic persona-augmentation or memory retrieval. Negative or adversarial attributes are systematically underrepresented due to both optimization inertia and low internal attention allocation (Jun et al., 8 Jan 2026).

6. Applications, Mitigations, and Future Directions

Role-Conditioned Value Attribution has direct implications for deploying LLMs in value-sensitive applications—ethical reasoning, multi-stakeholder dialogue, conversational agents, and legal decision-support. Persistent value inertia and role-driven motivated reasoning necessitate new mitigation strategies:

• Role-conditioned fine-tuning: Incorporating explicit (role, target) supervision to modify $f_\theta(Q; r)$ mappings.
• Loss re-weighting and adapters: Penalizing insufficient deviation from baseline or inserting role-aware modules.
• Prompt-design guidelines: Positioning role plays at prompt start, maximizing causal attention with selected adverbs/adjectives, and maintaining fixed-length prompt templates (Wang et al., 20 Oct 2025).
• Benchmark evolution: New evaluation suites emphasizing negative valence, adversarial motivation, and attribution consistency across long-range interactions (Jun et al., 8 Jan 2026).

Open research questions include the efficacy of adversarial persona prompts, effectiveness of persona-role pre-training, and sensitivity in multilingual or culturally diverse models (Lee et al., 2024). Addressing these will be crucial for robust, flexible, and context-sensitive value attribution in next-generation language systems.